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1. Comparison of Ce( iv )/Th( iv )-alkynyl complexes and observation of a trans -influence ligand series for Ce( iv )

   https://doi.org/10.1039/D5SC03222A  

   Yang, Qiaomu; Yu, Xiaojuan; Lapsheva, Ekaterina; Pandey, Pragati; Smith, Patrick W; Gupta, Himanshu; Gau, Michael R; Carroll, Patrick J; Minasian, Stefan G; Autschbach, Jochen; et al (August 2025, Chemical Science)

   Organometallic cerium(iv) and thorium(iv) alkynyl complexes were synthesized, and compared experimentally and theoretically. A cerium(iv)trans-influence ligand series was observed and analyzed. 

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2. M icro P rof : Code-level Attribution of Unnecessary Data Transfer in Microservice Applications

   https://doi.org/10.1145/3622787  

   Tariq, Syed Salauddin; Menard, Lance; Su, Pengfei; Roy, Probir (September 2023, ACM Transactions on Architecture and Code Optimization)

   The microservice architecture style has gained popularity due to its ability to fault isolation, ease of scaling applications, and developer’s agility. However, writing applications in the microservice design style has its challenges. Due to the loosely coupled nature, services communicate with others through standard communication APIs. This incurs significant overhead in the application due to communication protocol and data transformation. An inefficient service communication at the microservice application logic can further overwhelm the application. We perform a grey literature review showing that unnecessary data transfer is a real challenge in the industry. To the best of our knowledge, no effective tool is currently available to accurately identify the origins of unnecessary microservice communications that lead to significant performance overhead and provide guidance for optimization. To bridge the knowledge gap, we propose MicroProf, a dynamic program analysis tool to detect unnecessary data transfer in Java-based microservice applications. At the implementation level, MicroProfproposes novel techniques such as remote object sampling and hardware debug registers to monitor remote object usage. MicroProfreports the unnecessary data transfer at the application source code level. Furthermore, MicroProfpinpoints the opportunities for communication API optimization. MicroProfis evaluated on four well-known applications involving two real-world applications and two benchmarks, identifying five inefficient remote invocations. Guided by MicroProf, API optimization achieves an 87.5% reduction in the number of fields within REST API responses. The empirical evaluation further reveals that the optimized services experience a speedup of up to 4.59 ×. 

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3. Toward Robustness across Cosmological Simulation Models I llustris TNG, SIMBA, A strid , and S wift -E agle

   https://doi.org/10.3847/1538-4357/adec78  

   Jo, Yongseok; Genel, Shy; Sengupta, Anirvan; Wandelt, Benjamin; Somerville, Rachel; Villaescusa-Navarro, Francisco (September 2025, The Astrophysical Journal)

   Abstract The rapid advancement of large-scale cosmological simulations has opened new avenues for cosmological and astrophysical research. However, the increasing diversity among cosmological simulation models presents a challenge to therobustness. In this work, we develop the Model-Insensitive ESTimator (Miest), a machine that canrobustlyestimate the cosmological parameters, Ω_mandσ₈, from neural hydrogen maps of simulation models in the Cosmology and Astrophysics with MachinE Learning Simulations project—IllustrisTNG,SIMBA, Astrid, and SWIFT-Eagle. An estimator is consideredrobustif it possesses a consistent predictive power across all simulations, including those used during the training phase. We train our machine using multiple simulation models and ensure that it only extracts common features between the models while disregarding the model-specific features. This allows us to develop a novel model that is capable of accurately estimating parameters across a range of simulation models, without being biased toward any particular model. Upon the investigation of the latent space—a set of summary statistics, we find that the implementation ofrobustnessleads to the blending of latent variables across different models, demonstrating the removal of model-specific features. In comparison to a standard machine lackingrobustness, the average performance of Mieston the unseen simulations during the training phase has been improved by ∼17% for Ω_mand 38% forσ₈. By using a machine learning approach that can extractrobust, yet physical features, we hope to improve our understanding of galaxy formation and evolution in a (subgrid) model-insensitive manner, and ultimately, gain insight into the underlying physical processes responsible forrobustness. 

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4. Identifying charge-transfer and trip–multiplet states in Co( i ), Co( ii ), and Co( iii ) phthalocyanines using (magneto)optical spectroscopy and (TD)DFT calculations

   https://doi.org/10.1039/d5dt00628g  

   Muldowney, Breanna E; Nevonen, Dustin E; Jeaydi, Towhidi Illius; Ziegler, Christopher J; McNicholas, Brendon J; Nemykin, Victor N (January 2025, Dalton Transactions)

   The electronic structures, charge-transfer, and triplet–multiplet transitions in cobalt(i), cobalt(ii), and cobalt(iii) phthalocyanines were investigated in detail by UV-vis-NIR, MCD, DFT, and TDDFT methods. 

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5. Exploring sulfur donor atom coordination chemistry with La( ii ), Nd( ii ), and Tm( ii ) using a terphenylthiolate ligand

   https://doi.org/10.1039/d4cc01037j  

   Gilbert-Bass, Kito; Stennett, Cary R.; Grotjahn, Robin; Ziller, Joseph W.; Furche, Filipp; Evans, William J. (April 2024, Chemical Communications)

   To expand the range of donor atoms known to stabilize 4fⁿ5d¹Ln(ii) ions beyond C, N, and O first row main group donor atoms, the Ln(iii) terphenylthiolate iodides, Ln^III(SAr^iPr6)₂I (Ar^iPr6= C₆H₃-2,6-(C₆H₂-2,4,6-ⁱPr₃)₂, Ln = La, Nd) were reduced to Ln^II(SAr^iPr6)₂complexes. 

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